Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations20000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.7 MiB
Average record size in memory1.3 KiB

Variable types

Numeric7
Categorical23

Alerts

Numero_Visitas_Urgencias_Ultimo_Año has 1994 (10.0%) zerosZeros
Numero_de_Consultas_Post_Alta has 1979 (9.9%) zerosZeros

Reproduction

Analysis started2024-09-03 21:54:19.997105
Analysis finished2024-09-03 21:54:25.609698
Duration5.61 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Edad
Real number (ℝ)

Distinct72
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.46745
Minimum18
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2024-09-03T15:54:25.677403image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q135
median53
Q371
95-th percentile86
Maximum89
Range71
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.773984
Coefficient of variation (CV)0.38853516
Kurtosis-1.2134348
Mean53.46745
Median Absolute Deviation (MAD)18
Skewness0.0026060273
Sum1069349
Variance431.55842
MonotonicityNot monotonic
2024-09-03T15:54:25.775713image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 331
 
1.7%
32 318
 
1.6%
65 313
 
1.6%
38 309
 
1.5%
30 306
 
1.5%
71 303
 
1.5%
55 303
 
1.5%
35 303
 
1.5%
79 302
 
1.5%
68 300
 
1.5%
Other values (62) 16912
84.6%
ValueCountFrequency (%)
18 267
1.3%
19 277
1.4%
20 293
1.5%
21 264
1.3%
22 252
1.3%
23 286
1.4%
24 257
1.3%
25 286
1.4%
26 281
1.4%
27 299
1.5%
ValueCountFrequency (%)
89 272
1.4%
88 276
1.4%
87 257
1.3%
86 279
1.4%
85 278
1.4%
84 261
1.3%
83 279
1.4%
82 273
1.4%
81 266
1.3%
80 292
1.5%

Genero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
10171 
0
9829 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 10171
50.9%
0 9829
49.1%

Length

2024-09-03T15:54:25.853264image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:25.920650image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 10171
50.9%
0 9829
49.1%

Most occurring characters

ValueCountFrequency (%)
1 10171
50.9%
0 9829
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10171
50.9%
0 9829
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10171
50.9%
0 9829
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10171
50.9%
0 9829
49.1%
Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0546
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2024-09-03T15:54:25.986841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q322
95-th percentile28
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.3959778
Coefficient of variation (CV)0.55770182
Kurtosis-1.2230743
Mean15.0546
Median Absolute Deviation (MAD)7
Skewness-0.0052229378
Sum301092
Variance70.492443
MonotonicityNot monotonic
2024-09-03T15:54:26.121859image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
26 767
 
3.8%
4 742
 
3.7%
23 726
 
3.6%
24 718
 
3.6%
10 716
 
3.6%
20 712
 
3.6%
7 711
 
3.6%
6 708
 
3.5%
2 707
 
3.5%
22 703
 
3.5%
Other values (19) 12790
63.9%
ValueCountFrequency (%)
1 657
3.3%
2 707
3.5%
3 658
3.3%
4 742
3.7%
5 651
3.3%
6 708
3.5%
7 711
3.6%
8 700
3.5%
9 687
3.4%
10 716
3.6%
ValueCountFrequency (%)
29 682
3.4%
28 699
3.5%
27 695
3.5%
26 767
3.8%
25 658
3.3%
24 718
3.6%
23 726
3.6%
22 703
3.5%
21 689
3.4%
20 712
3.6%
Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4869
Minimum0
Maximum9
Zeros1994
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2024-09-03T15:54:26.189789image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8629073
Coefficient of variation (CV)0.63805908
Kurtosis-1.2172602
Mean4.4869
Median Absolute Deviation (MAD)2
Skewness0.0051370562
Sum89738
Variance8.1962382
MonotonicityNot monotonic
2024-09-03T15:54:26.256756image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 2069
10.3%
8 2053
10.3%
3 2015
10.1%
4 2008
10.0%
2 2006
10.0%
1 2000
10.0%
0 1994
10.0%
6 1981
9.9%
9 1938
9.7%
7 1936
9.7%
ValueCountFrequency (%)
0 1994
10.0%
1 2000
10.0%
2 2006
10.0%
3 2015
10.1%
4 2008
10.0%
5 2069
10.3%
6 1981
9.9%
7 1936
9.7%
8 2053
10.3%
9 1938
9.7%
ValueCountFrequency (%)
9 1938
9.7%
8 2053
10.3%
7 1936
9.7%
6 1981
9.9%
5 2069
10.3%
4 2008
10.0%
3 2015
10.1%
2 2006
10.0%
1 2000
10.0%
0 1994
10.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2
5037 
4
5035 
1
4984 
3
4944 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row4
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 5037
25.2%
4 5035
25.2%
1 4984
24.9%
3 4944
24.7%

Length

2024-09-03T15:54:26.323793image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:26.390919image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 5037
25.2%
4 5035
25.2%
1 4984
24.9%
3 4944
24.7%

Most occurring characters

ValueCountFrequency (%)
2 5037
25.2%
4 5035
25.2%
1 4984
24.9%
3 4944
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5037
25.2%
4 5035
25.2%
1 4984
24.9%
3 4944
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5037
25.2%
4 5035
25.2%
1 4984
24.9%
3 4944
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5037
25.2%
4 5035
25.2%
1 4984
24.9%
3 4944
24.7%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Ninguna
5120 
Dos
5034 
Una
4990 
Tres o más
4856 

Length

Max length10
Median length3
Mean length5.7236
Min length3

Characters and Unicode

Total characters114472
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNinguna
2nd rowTres o más
3rd rowTres o más
4th rowUna
5th rowUna

Common Values

ValueCountFrequency (%)
Ninguna 5120
25.6%
Dos 5034
25.2%
Una 4990
24.9%
Tres o más 4856
24.3%

Length

2024-09-03T15:54:26.470597image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:26.541484image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ninguna 5120
17.2%
dos 5034
16.9%
una 4990
16.8%
tres 4856
16.3%
o 4856
16.3%
más 4856
16.3%

Most occurring characters

ValueCountFrequency (%)
n 15230
13.3%
s 14746
12.9%
a 10110
 
8.8%
o 9890
 
8.6%
9712
 
8.5%
N 5120
 
4.5%
i 5120
 
4.5%
g 5120
 
4.5%
u 5120
 
4.5%
D 5034
 
4.4%
Other values (6) 29270
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 114472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 15230
13.3%
s 14746
12.9%
a 10110
 
8.8%
o 9890
 
8.6%
9712
 
8.5%
N 5120
 
4.5%
i 5120
 
4.5%
g 5120
 
4.5%
u 5120
 
4.5%
D 5034
 
4.4%
Other values (6) 29270
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 114472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 15230
13.3%
s 14746
12.9%
a 10110
 
8.8%
o 9890
 
8.6%
9712
 
8.5%
N 5120
 
4.5%
i 5120
 
4.5%
g 5120
 
4.5%
u 5120
 
4.5%
D 5034
 
4.4%
Other values (6) 29270
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 114472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 15230
13.3%
s 14746
12.9%
a 10110
 
8.8%
o 9890
 
8.6%
9712
 
8.5%
N 5120
 
4.5%
i 5120
 
4.5%
g 5120
 
4.5%
u 5120
 
4.5%
D 5034
 
4.4%
Other values (6) 29270
25.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Bajo
5051 
Ninguno
5030 
Alto
4977 
Medio
4942 

Length

Max length7
Median length4
Mean length5.0016
Min length4

Characters and Unicode

Total characters100032
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlto
2nd rowNinguno
3rd rowBajo
4th rowNinguno
5th rowAlto

Common Values

ValueCountFrequency (%)
Bajo 5051
25.3%
Ninguno 5030
25.1%
Alto 4977
24.9%
Medio 4942
24.7%

Length

2024-09-03T15:54:26.619913image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:26.690212image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
bajo 5051
25.3%
ninguno 5030
25.1%
alto 4977
24.9%
medio 4942
24.7%

Most occurring characters

ValueCountFrequency (%)
o 20000
20.0%
n 10060
10.1%
i 9972
10.0%
B 5051
 
5.0%
a 5051
 
5.0%
j 5051
 
5.0%
N 5030
 
5.0%
g 5030
 
5.0%
u 5030
 
5.0%
A 4977
 
5.0%
Other values (5) 24780
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 20000
20.0%
n 10060
10.1%
i 9972
10.0%
B 5051
 
5.0%
a 5051
 
5.0%
j 5051
 
5.0%
N 5030
 
5.0%
g 5030
 
5.0%
u 5030
 
5.0%
A 4977
 
5.0%
Other values (5) 24780
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 20000
20.0%
n 10060
10.1%
i 9972
10.0%
B 5051
 
5.0%
a 5051
 
5.0%
j 5051
 
5.0%
N 5030
 
5.0%
g 5030
 
5.0%
u 5030
 
5.0%
A 4977
 
5.0%
Other values (5) 24780
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 20000
20.0%
n 10060
10.1%
i 9972
10.0%
B 5051
 
5.0%
a 5051
 
5.0%
j 5051
 
5.0%
N 5030
 
5.0%
g 5030
 
5.0%
u 5030
 
5.0%
A 4977
 
5.0%
Other values (5) 24780
24.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Baja
6727 
Buena
6699 
Moderada
6574 

Length

Max length8
Median length5
Mean length5.64975
Min length4

Characters and Unicode

Total characters112995
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBaja
2nd rowBaja
3rd rowBuena
4th rowModerada
5th rowBaja

Common Values

ValueCountFrequency (%)
Baja 6727
33.6%
Buena 6699
33.5%
Moderada 6574
32.9%

Length

2024-09-03T15:54:26.775512image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:26.845959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
baja 6727
33.6%
buena 6699
33.5%
moderada 6574
32.9%

Most occurring characters

ValueCountFrequency (%)
a 33301
29.5%
B 13426
11.9%
e 13273
 
11.7%
d 13148
 
11.6%
j 6727
 
6.0%
u 6699
 
5.9%
n 6699
 
5.9%
M 6574
 
5.8%
o 6574
 
5.8%
r 6574
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 33301
29.5%
B 13426
11.9%
e 13273
 
11.7%
d 13148
 
11.6%
j 6727
 
6.0%
u 6699
 
5.9%
n 6699
 
5.9%
M 6574
 
5.8%
o 6574
 
5.8%
r 6574
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 33301
29.5%
B 13426
11.9%
e 13273
 
11.7%
d 13148
 
11.6%
j 6727
 
6.0%
u 6699
 
5.9%
n 6699
 
5.9%
M 6574
 
5.8%
o 6574
 
5.8%
r 6574
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 33301
29.5%
B 13426
11.9%
e 13273
 
11.7%
d 13148
 
11.6%
j 6727
 
6.0%
u 6699
 
5.9%
n 6699
 
5.9%
M 6574
 
5.8%
o 6574
 
5.8%
r 6574
 
5.8%

Soporte_Familiar
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Moderado
6737 
Bueno
6643 
Bajo
6620 

Length

Max length8
Median length5
Mean length5.67955
Min length4

Characters and Unicode

Total characters113591
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerado
2nd rowBueno
3rd rowBajo
4th rowModerado
5th rowBueno

Common Values

ValueCountFrequency (%)
Moderado 6737
33.7%
Bueno 6643
33.2%
Bajo 6620
33.1%

Length

2024-09-03T15:54:26.927827image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:26.998791image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
moderado 6737
33.7%
bueno 6643
33.2%
bajo 6620
33.1%

Most occurring characters

ValueCountFrequency (%)
o 26737
23.5%
d 13474
11.9%
e 13380
11.8%
a 13357
11.8%
B 13263
11.7%
M 6737
 
5.9%
r 6737
 
5.9%
u 6643
 
5.8%
n 6643
 
5.8%
j 6620
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 26737
23.5%
d 13474
11.9%
e 13380
11.8%
a 13357
11.8%
B 13263
11.7%
M 6737
 
5.9%
r 6737
 
5.9%
u 6643
 
5.8%
n 6643
 
5.8%
j 6620
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 26737
23.5%
d 13474
11.9%
e 13380
11.8%
a 13357
11.8%
B 13263
11.7%
M 6737
 
5.9%
r 6737
 
5.9%
u 6643
 
5.8%
n 6643
 
5.8%
j 6620
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 26737
23.5%
d 13474
11.9%
e 13380
11.8%
a 13357
11.8%
B 13263
11.7%
M 6737
 
5.9%
r 6737
 
5.9%
u 6643
 
5.8%
n 6643
 
5.8%
j 6620
 
5.8%

Reingreso
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
10044 
0
9956 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 10044
50.2%
0 9956
49.8%

Length

2024-09-03T15:54:27.067940image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:27.130345image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 10044
50.2%
0 9956
49.8%

Most occurring characters

ValueCountFrequency (%)
1 10044
50.2%
0 9956
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10044
50.2%
0 9956
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10044
50.2%
0 9956
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10044
50.2%
0 9956
49.8%

Tipo_de_Seguro
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
6692 
0
6679 
2
6629 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1 6692
33.5%
0 6679
33.4%
2 6629
33.1%

Length

2024-09-03T15:54:27.195936image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:27.261144image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6692
33.5%
0 6679
33.4%
2 6629
33.1%

Most occurring characters

ValueCountFrequency (%)
1 6692
33.5%
0 6679
33.4%
2 6629
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6692
33.5%
0 6679
33.4%
2 6629
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6692
33.5%
0 6679
33.4%
2 6629
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6692
33.5%
0 6679
33.4%
2 6629
33.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Universitaria
5046 
Secundaria
5010 
Sin educación
4974 
Primaria
4970 

Length

Max length13
Median length13
Mean length11.006
Min length8

Characters and Unicode

Total characters220120
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSin educación
2nd rowPrimaria
3rd rowPrimaria
4th rowSin educación
5th rowSin educación

Common Values

ValueCountFrequency (%)
Universitaria 5046
25.2%
Secundaria 5010
25.1%
Sin educación 4974
24.9%
Primaria 4970
24.9%

Length

2024-09-03T15:54:27.334238image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:27.404878image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
universitaria 5046
20.2%
secundaria 5010
20.1%
sin 4974
19.9%
educación 4974
19.9%
primaria 4970
19.9%

Most occurring characters

ValueCountFrequency (%)
i 40036
18.2%
a 35026
15.9%
r 25042
11.4%
n 20004
9.1%
e 15030
 
6.8%
c 14958
 
6.8%
S 9984
 
4.5%
d 9984
 
4.5%
u 9984
 
4.5%
U 5046
 
2.3%
Other values (7) 35026
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 220120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 40036
18.2%
a 35026
15.9%
r 25042
11.4%
n 20004
9.1%
e 15030
 
6.8%
c 14958
 
6.8%
S 9984
 
4.5%
d 9984
 
4.5%
u 9984
 
4.5%
U 5046
 
2.3%
Other values (7) 35026
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 220120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 40036
18.2%
a 35026
15.9%
r 25042
11.4%
n 20004
9.1%
e 15030
 
6.8%
c 14958
 
6.8%
S 9984
 
4.5%
d 9984
 
4.5%
u 9984
 
4.5%
U 5046
 
2.3%
Other values (7) 35026
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 220120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 40036
18.2%
a 35026
15.9%
r 25042
11.4%
n 20004
9.1%
e 15030
 
6.8%
c 14958
 
6.8%
S 9984
 
4.5%
d 9984
 
4.5%
u 9984
 
4.5%
U 5046
 
2.3%
Other values (7) 35026
15.9%

Indice_de_Masa_Corporal
Real number (ℝ)

Distinct2499
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.573967
Minimum15
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2024-09-03T15:54:27.488802image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.25
Q121.31
median27.56
Q333.8425
95-th percentile38.6905
Maximum40
Range25
Interquartile range (IQR)12.5325

Descriptive statistics

Standard deviation7.2311586
Coefficient of variation (CV)0.26224586
Kurtosis-1.212632
Mean27.573967
Median Absolute Deviation (MAD)6.27
Skewness-0.015347548
Sum551479.34
Variance52.289654
MonotonicityNot monotonic
2024-09-03T15:54:27.583424image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.37 20
 
0.1%
22.55 19
 
0.1%
26.75 18
 
0.1%
32.3 18
 
0.1%
19.66 18
 
0.1%
35.68 17
 
0.1%
26.7 17
 
0.1%
33.23 17
 
0.1%
20.87 17
 
0.1%
31.5 17
 
0.1%
Other values (2489) 19822
99.1%
ValueCountFrequency (%)
15 3
 
< 0.1%
15.01 8
< 0.1%
15.02 5
< 0.1%
15.03 5
< 0.1%
15.04 12
0.1%
15.05 6
< 0.1%
15.06 5
< 0.1%
15.07 7
< 0.1%
15.08 5
< 0.1%
15.09 9
< 0.1%
ValueCountFrequency (%)
40 3
 
< 0.1%
39.99 5
< 0.1%
39.98 1
 
< 0.1%
39.97 7
< 0.1%
39.96 6
< 0.1%
39.95 8
< 0.1%
39.94 12
0.1%
39.93 6
< 0.1%
39.92 6
< 0.1%
39.91 11
0.1%

Estado_Mental
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Sin problemas conocidos
6700 
Ansiedad
6671 
Depresión
6629 

Length

Max length23
Median length9
Mean length13.35645
Min length8

Characters and Unicode

Total characters267129
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSin problemas conocidos
2nd rowAnsiedad
3rd rowDepresión
4th rowAnsiedad
5th rowDepresión

Common Values

ValueCountFrequency (%)
Sin problemas conocidos 6700
33.5%
Ansiedad 6671
33.4%
Depresión 6629
33.1%

Length

2024-09-03T15:54:27.668344image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:27.737807image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
sin 6700
20.1%
problemas 6700
20.1%
conocidos 6700
20.1%
ansiedad 6671
20.0%
depresión 6629
19.8%

Most occurring characters

ValueCountFrequency (%)
o 26800
10.0%
n 26700
10.0%
i 26700
10.0%
s 26700
10.0%
e 26629
10.0%
d 20042
 
7.5%
13400
 
5.0%
c 13400
 
5.0%
a 13371
 
5.0%
p 13329
 
5.0%
Other values (8) 60058
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 267129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 26800
10.0%
n 26700
10.0%
i 26700
10.0%
s 26700
10.0%
e 26629
10.0%
d 20042
 
7.5%
13400
 
5.0%
c 13400
 
5.0%
a 13371
 
5.0%
p 13329
 
5.0%
Other values (8) 60058
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 267129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 26800
10.0%
n 26700
10.0%
i 26700
10.0%
s 26700
10.0%
e 26629
10.0%
d 20042
 
7.5%
13400
 
5.0%
c 13400
 
5.0%
a 13371
 
5.0%
p 13329
 
5.0%
Other values (8) 60058
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 267129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 26800
10.0%
n 26700
10.0%
i 26700
10.0%
s 26700
10.0%
e 26629
10.0%
d 20042
 
7.5%
13400
 
5.0%
c 13400
 
5.0%
a 13371
 
5.0%
p 13329
 
5.0%
Other values (8) 60058
22.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Ninguna
5051 
Moderada
5002 
Alta
4986 
Baja
4961 

Length

Max length8
Median length7
Mean length5.75805
Min length4

Characters and Unicode

Total characters115161
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlta
2nd rowModerada
3rd rowNinguna
4th rowBaja
5th rowBaja

Common Values

ValueCountFrequency (%)
Ninguna 5051
25.3%
Moderada 5002
25.0%
Alta 4986
24.9%
Baja 4961
24.8%

Length

2024-09-03T15:54:27.820334image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:27.895799image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ninguna 5051
25.3%
moderada 5002
25.0%
alta 4986
24.9%
baja 4961
24.8%

Most occurring characters

ValueCountFrequency (%)
a 29963
26.0%
n 10102
 
8.8%
d 10004
 
8.7%
N 5051
 
4.4%
i 5051
 
4.4%
g 5051
 
4.4%
u 5051
 
4.4%
M 5002
 
4.3%
o 5002
 
4.3%
e 5002
 
4.3%
Other values (6) 29882
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115161
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 29963
26.0%
n 10102
 
8.8%
d 10004
 
8.7%
N 5051
 
4.4%
i 5051
 
4.4%
g 5051
 
4.4%
u 5051
 
4.4%
M 5002
 
4.3%
o 5002
 
4.3%
e 5002
 
4.3%
Other values (6) 29882
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115161
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 29963
26.0%
n 10102
 
8.8%
d 10004
 
8.7%
N 5051
 
4.4%
i 5051
 
4.4%
g 5051
 
4.4%
u 5051
 
4.4%
M 5002
 
4.3%
o 5002
 
4.3%
e 5002
 
4.3%
Other values (6) 29882
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115161
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 29963
26.0%
n 10102
 
8.8%
d 10004
 
8.7%
N 5051
 
4.4%
i 5051
 
4.4%
g 5051
 
4.4%
u 5051
 
4.4%
M 5002
 
4.3%
o 5002
 
4.3%
e 5002
 
4.3%
Other values (6) 29882
25.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Fumador
10046 
No fumador
9954 

Length

Max length10
Median length7
Mean length8.4931
Min length7

Characters and Unicode

Total characters169862
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFumador
2nd rowNo fumador
3rd rowFumador
4th rowNo fumador
5th rowNo fumador

Common Values

ValueCountFrequency (%)
Fumador 10046
50.2%
No fumador 9954
49.8%

Length

2024-09-03T15:54:28.024830image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:28.091071image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
fumador 20000
66.8%
no 9954
33.2%

Most occurring characters

ValueCountFrequency (%)
o 29954
17.6%
u 20000
11.8%
m 20000
11.8%
a 20000
11.8%
d 20000
11.8%
r 20000
11.8%
F 10046
 
5.9%
N 9954
 
5.9%
9954
 
5.9%
f 9954
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 169862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 29954
17.6%
u 20000
11.8%
m 20000
11.8%
a 20000
11.8%
d 20000
11.8%
r 20000
11.8%
F 10046
 
5.9%
N 9954
 
5.9%
9954
 
5.9%
f 9954
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 169862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 29954
17.6%
u 20000
11.8%
m 20000
11.8%
a 20000
11.8%
d 20000
11.8%
r 20000
11.8%
F 10046
 
5.9%
N 9954
 
5.9%
9954
 
5.9%
f 9954
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 169862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 29954
17.6%
u 20000
11.8%
m 20000
11.8%
a 20000
11.8%
d 20000
11.8%
r 20000
11.8%
F 10046
 
5.9%
N 9954
 
5.9%
9954
 
5.9%
f 9954
 
5.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Alto
5049 
Moderado
5015 
Bajo
4993 
Ninguno
4943 

Length

Max length8
Median length4
Mean length5.74445
Min length4

Characters and Unicode

Total characters114889
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlto
2nd rowModerado
3rd rowAlto
4th rowNinguno
5th rowAlto

Common Values

ValueCountFrequency (%)
Alto 5049
25.2%
Moderado 5015
25.1%
Bajo 4993
25.0%
Ninguno 4943
24.7%

Length

2024-09-03T15:54:28.169689image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:28.242686image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
alto 5049
25.2%
moderado 5015
25.1%
bajo 4993
25.0%
ninguno 4943
24.7%

Most occurring characters

ValueCountFrequency (%)
o 25015
21.8%
d 10030
 
8.7%
a 10008
 
8.7%
n 9886
 
8.6%
A 5049
 
4.4%
l 5049
 
4.4%
t 5049
 
4.4%
M 5015
 
4.4%
e 5015
 
4.4%
r 5015
 
4.4%
Other values (6) 29758
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 114889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 25015
21.8%
d 10030
 
8.7%
a 10008
 
8.7%
n 9886
 
8.6%
A 5049
 
4.4%
l 5049
 
4.4%
t 5049
 
4.4%
M 5015
 
4.4%
e 5015
 
4.4%
r 5015
 
4.4%
Other values (6) 29758
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 114889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 25015
21.8%
d 10030
 
8.7%
a 10008
 
8.7%
n 9886
 
8.6%
A 5049
 
4.4%
l 5049
 
4.4%
t 5049
 
4.4%
M 5015
 
4.4%
e 5015
 
4.4%
r 5015
 
4.4%
Other values (6) 29758
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 114889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 25015
21.8%
d 10030
 
8.7%
a 10008
 
8.7%
n 9886
 
8.6%
A 5049
 
4.4%
l 5049
 
4.4%
t 5049
 
4.4%
M 5015
 
4.4%
e 5015
 
4.4%
r 5015
 
4.4%
Other values (6) 29758
25.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Empleado
6762 
Jubilado
6656 
Desempleado
6582 

Length

Max length11
Median length8
Mean length8.9873
Min length8

Characters and Unicode

Total characters179746
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmpleado
2nd rowEmpleado
3rd rowDesempleado
4th rowDesempleado
5th rowDesempleado

Common Values

ValueCountFrequency (%)
Empleado 6762
33.8%
Jubilado 6656
33.3%
Desempleado 6582
32.9%

Length

2024-09-03T15:54:28.321993image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:28.391597image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
empleado 6762
33.8%
jubilado 6656
33.3%
desempleado 6582
32.9%

Most occurring characters

ValueCountFrequency (%)
e 26508
14.7%
l 20000
11.1%
a 20000
11.1%
d 20000
11.1%
o 20000
11.1%
m 13344
7.4%
p 13344
7.4%
E 6762
 
3.8%
J 6656
 
3.7%
u 6656
 
3.7%
Other values (4) 26476
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 179746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 26508
14.7%
l 20000
11.1%
a 20000
11.1%
d 20000
11.1%
o 20000
11.1%
m 13344
7.4%
p 13344
7.4%
E 6762
 
3.8%
J 6656
 
3.7%
u 6656
 
3.7%
Other values (4) 26476
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 179746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 26508
14.7%
l 20000
11.1%
a 20000
11.1%
d 20000
11.1%
o 20000
11.1%
m 13344
7.4%
p 13344
7.4%
E 6762
 
3.8%
J 6656
 
3.7%
u 6656
 
3.7%
Other values (4) 26476
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 179746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 26508
14.7%
l 20000
11.1%
a 20000
11.1%
d 20000
11.1%
o 20000
11.1%
m 13344
7.4%
p 13344
7.4%
E 6762
 
3.8%
J 6656
 
3.7%
u 6656
 
3.7%
Other values (4) 26476
14.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Mala
6720 
Buena
6653 
Regular
6627 

Length

Max length7
Median length5
Mean length5.3267
Min length4

Characters and Unicode

Total characters106534
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMala
2nd rowMala
3rd rowBuena
4th rowBuena
5th rowMala

Common Values

ValueCountFrequency (%)
Mala 6720
33.6%
Buena 6653
33.3%
Regular 6627
33.1%

Length

2024-09-03T15:54:28.469172image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:28.538232image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
mala 6720
33.6%
buena 6653
33.3%
regular 6627
33.1%

Most occurring characters

ValueCountFrequency (%)
a 26720
25.1%
l 13347
12.5%
u 13280
12.5%
e 13280
12.5%
M 6720
 
6.3%
B 6653
 
6.2%
n 6653
 
6.2%
R 6627
 
6.2%
g 6627
 
6.2%
r 6627
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 26720
25.1%
l 13347
12.5%
u 13280
12.5%
e 13280
12.5%
M 6720
 
6.3%
B 6653
 
6.2%
n 6653
 
6.2%
R 6627
 
6.2%
g 6627
 
6.2%
r 6627
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 26720
25.1%
l 13347
12.5%
u 13280
12.5%
e 13280
12.5%
M 6720
 
6.3%
B 6653
 
6.2%
n 6653
 
6.2%
R 6627
 
6.2%
g 6627
 
6.2%
r 6627
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 26720
25.1%
l 13347
12.5%
u 13280
12.5%
e 13280
12.5%
M 6720
 
6.3%
B 6653
 
6.2%
n 6653
 
6.2%
R 6627
 
6.2%
g 6627
 
6.2%
r 6627
 
6.2%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2
4072 
4
4013 
1
3995 
3
3973 
0
3947 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4072
20.4%
4 4013
20.1%
1 3995
20.0%
3 3973
19.9%
0 3947
19.7%

Length

2024-09-03T15:54:28.606426image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:28.676722image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 4072
20.4%
4 4013
20.1%
1 3995
20.0%
3 3973
19.9%
0 3947
19.7%

Most occurring characters

ValueCountFrequency (%)
2 4072
20.4%
4 4013
20.1%
1 3995
20.0%
3 3973
19.9%
0 3947
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4072
20.4%
4 4013
20.1%
1 3995
20.0%
3 3973
19.9%
0 3947
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4072
20.4%
4 4013
20.1%
1 3995
20.0%
3 3973
19.9%
0 3947
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4072
20.4%
4 4013
20.1%
1 3995
20.0%
3 3973
19.9%
0 3947
19.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
No
10040 
Sí
9960 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters40000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
No 10040
50.2%
Sí 9960
49.8%

Length

2024-09-03T15:54:28.748756image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:28.811095image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
no 10040
50.2%
sí 9960
49.8%

Most occurring characters

ValueCountFrequency (%)
N 10040
25.1%
o 10040
25.1%
S 9960
24.9%
í 9960
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 10040
25.1%
o 10040
25.1%
S 9960
24.9%
í 9960
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 10040
25.1%
o 10040
25.1%
S 9960
24.9%
í 9960
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 10040
25.1%
o 10040
25.1%
S 9960
24.9%
í 9960
24.9%
Distinct59
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.05125
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2024-09-03T15:54:28.886953image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q115
median30
Q345
95-th percentile57
Maximum59
Range58
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.983564
Coefficient of variation (CV)0.56515333
Kurtosis-1.1924562
Mean30.05125
Median Absolute Deviation (MAD)15
Skewness-0.0034783052
Sum601025
Variance288.44145
MonotonicityNot monotonic
2024-09-03T15:54:28.983481image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 387
 
1.9%
29 379
 
1.9%
48 374
 
1.9%
8 367
 
1.8%
30 366
 
1.8%
13 365
 
1.8%
16 365
 
1.8%
57 364
 
1.8%
33 362
 
1.8%
17 359
 
1.8%
Other values (49) 16312
81.6%
ValueCountFrequency (%)
1 323
1.6%
2 343
1.7%
3 333
1.7%
4 333
1.7%
5 341
1.7%
6 333
1.7%
7 344
1.7%
8 367
1.8%
9 315
1.6%
10 343
1.7%
ValueCountFrequency (%)
59 324
1.6%
58 346
1.7%
57 364
1.8%
56 340
1.7%
55 315
1.6%
54 341
1.7%
53 357
1.8%
52 300
1.5%
51 329
1.6%
50 351
1.8%

Apoyo_Social
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Bajo
6753 
Medio
6650 
Alto
6597 

Length

Max length5
Median length4
Mean length4.3325
Min length4

Characters and Unicode

Total characters86650
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBajo
2nd rowBajo
3rd rowAlto
4th rowAlto
5th rowAlto

Common Values

ValueCountFrequency (%)
Bajo 6753
33.8%
Medio 6650
33.2%
Alto 6597
33.0%

Length

2024-09-03T15:54:29.066776image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:29.134528image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
bajo 6753
33.8%
medio 6650
33.2%
alto 6597
33.0%

Most occurring characters

ValueCountFrequency (%)
o 20000
23.1%
B 6753
 
7.8%
a 6753
 
7.8%
j 6753
 
7.8%
M 6650
 
7.7%
e 6650
 
7.7%
d 6650
 
7.7%
i 6650
 
7.7%
A 6597
 
7.6%
l 6597
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 20000
23.1%
B 6753
 
7.8%
a 6753
 
7.8%
j 6753
 
7.8%
M 6650
 
7.7%
e 6650
 
7.7%
d 6650
 
7.7%
i 6650
 
7.7%
A 6597
 
7.6%
l 6597
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 20000
23.1%
B 6753
 
7.8%
a 6753
 
7.8%
j 6753
 
7.8%
M 6650
 
7.7%
e 6650
 
7.7%
d 6650
 
7.7%
i 6650
 
7.7%
A 6597
 
7.6%
l 6597
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 20000
23.1%
B 6753
 
7.8%
a 6753
 
7.8%
j 6753
 
7.8%
M 6650
 
7.7%
e 6650
 
7.7%
d 6650
 
7.7%
i 6650
 
7.7%
A 6597
 
7.6%
l 6597
 
7.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Enfermedad crónica
6722 
Cirugía
6666 
Emergencia
6612 

Length

Max length18
Median length10
Mean length11.6889
Min length7

Characters and Unicode

Total characters233778
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnfermedad crónica
2nd rowEmergencia
3rd rowEnfermedad crónica
4th rowEmergencia
5th rowEmergencia

Common Values

ValueCountFrequency (%)
Enfermedad crónica 6722
33.6%
Cirugía 6666
33.3%
Emergencia 6612
33.1%

Length

2024-09-03T15:54:29.207904image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:29.275671image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
enfermedad 6722
25.2%
crónica 6722
25.2%
cirugía 6666
24.9%
emergencia 6612
24.7%

Most occurring characters

ValueCountFrequency (%)
r 26722
11.4%
a 26722
11.4%
e 26668
11.4%
n 20056
8.6%
c 20056
8.6%
i 20000
8.6%
d 13444
 
5.8%
E 13334
 
5.7%
m 13334
 
5.7%
g 13278
 
5.7%
Other values (6) 40164
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 233778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 26722
11.4%
a 26722
11.4%
e 26668
11.4%
n 20056
8.6%
c 20056
8.6%
i 20000
8.6%
d 13444
 
5.8%
E 13334
 
5.7%
m 13334
 
5.7%
g 13278
 
5.7%
Other values (6) 40164
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 233778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 26722
11.4%
a 26722
11.4%
e 26668
11.4%
n 20056
8.6%
c 20056
8.6%
i 20000
8.6%
d 13444
 
5.8%
E 13334
 
5.7%
m 13334
 
5.7%
g 13278
 
5.7%
Other values (6) 40164
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 233778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 26722
11.4%
a 26722
11.4%
e 26668
11.4%
n 20056
8.6%
c 20056
8.6%
i 20000
8.6%
d 13444
 
5.8%
E 13334
 
5.7%
m 13334
 
5.7%
g 13278
 
5.7%
Other values (6) 40164
17.2%

Numero_de_Consultas_Post_Alta
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.52015
Minimum0
Maximum9
Zeros1979
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2024-09-03T15:54:29.339126image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8586365
Coefficient of variation (CV)0.63242071
Kurtosis-1.2124698
Mean4.52015
Median Absolute Deviation (MAD)2
Skewness-0.015037711
Sum90403
Variance8.1718026
MonotonicityNot monotonic
2024-09-03T15:54:29.405680image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 2072
10.4%
5 2039
10.2%
6 2023
10.1%
4 2015
10.1%
2 1999
10.0%
3 1986
9.9%
8 1985
9.9%
0 1979
9.9%
9 1971
9.9%
1 1931
9.7%
ValueCountFrequency (%)
0 1979
9.9%
1 1931
9.7%
2 1999
10.0%
3 1986
9.9%
4 2015
10.1%
5 2039
10.2%
6 2023
10.1%
7 2072
10.4%
8 1985
9.9%
9 1971
9.9%
ValueCountFrequency (%)
9 1971
9.9%
8 1985
9.9%
7 2072
10.4%
6 2023
10.1%
5 2039
10.2%
4 2015
10.1%
3 1986
9.9%
2 1999
10.0%
1 1931
9.7%
0 1979
9.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
6716 
2
6663 
1
6621 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 6716
33.6%
2 6663
33.3%
1 6621
33.1%

Length

2024-09-03T15:54:29.473913image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:29.538976image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6716
33.6%
2 6663
33.3%
1 6621
33.1%

Most occurring characters

ValueCountFrequency (%)
0 6716
33.6%
2 6663
33.3%
1 6621
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6716
33.6%
2 6663
33.3%
1 6621
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6716
33.6%
2 6663
33.3%
1 6621
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6716
33.6%
2 6663
33.3%
1 6621
33.1%

Comorbilidades
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Sin comorbilidades
5106 
Cardiopatía
5082 
Hipertensión
4908 
Diabetes
4904 

Length

Max length18
Median length12
Mean length12.2969
Min length8

Characters and Unicode

Total characters245938
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSin comorbilidades
2nd rowDiabetes
3rd rowHipertensión
4th rowDiabetes
5th rowHipertensión

Common Values

ValueCountFrequency (%)
Sin comorbilidades 5106
25.5%
Cardiopatía 5082
25.4%
Hipertensión 4908
24.5%
Diabetes 4904
24.5%

Length

2024-09-03T15:54:29.611009image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:29.680658image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
sin 5106
20.3%
comorbilidades 5106
20.3%
cardiopatía 5082
20.2%
hipertensión 4908
19.5%
diabetes 4904
19.5%

Most occurring characters

ValueCountFrequency (%)
i 35120
14.3%
a 25256
10.3%
e 24730
10.1%
d 15294
 
6.2%
o 15294
 
6.2%
r 15096
 
6.1%
n 14922
 
6.1%
s 14918
 
6.1%
t 14894
 
6.1%
b 10010
 
4.1%
Other values (11) 60404
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 245938
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 35120
14.3%
a 25256
10.3%
e 24730
10.1%
d 15294
 
6.2%
o 15294
 
6.2%
r 15096
 
6.1%
n 14922
 
6.1%
s 14918
 
6.1%
t 14894
 
6.1%
b 10010
 
4.1%
Other values (11) 60404
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 245938
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 35120
14.3%
a 25256
10.3%
e 24730
10.1%
d 15294
 
6.2%
o 15294
 
6.2%
r 15096
 
6.1%
n 14922
 
6.1%
s 14918
 
6.1%
t 14894
 
6.1%
b 10010
 
4.1%
Other values (11) 60404
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 245938
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 35120
14.3%
a 25256
10.3%
e 24730
10.1%
d 15294
 
6.2%
o 15294
 
6.2%
r 15096
 
6.1%
n 14922
 
6.1%
s 14918
 
6.1%
t 14894
 
6.1%
b 10010
 
4.1%
Other values (11) 60404
24.6%

Distancia_Al_Hospital
Real number (ℝ)

Distinct4858
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.02645
Minimum0.51
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2024-09-03T15:54:29.816654image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.51
5-th percentile3.0395
Q112.65
median24.965
Q337.2325
95-th percentile47.51
Maximum50
Range49.49
Interquartile range (IQR)24.5825

Descriptive statistics

Standard deviation14.260407
Coefficient of variation (CV)0.56981342
Kurtosis-1.1948298
Mean25.02645
Median Absolute Deviation (MAD)12.3
Skewness0.022629714
Sum500529.01
Variance203.35922
MonotonicityNot monotonic
2024-09-03T15:54:29.910192image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.66 15
 
0.1%
49.8 13
 
0.1%
3.94 13
 
0.1%
21.93 12
 
0.1%
33.04 12
 
0.1%
26.19 12
 
0.1%
1.55 12
 
0.1%
3.56 11
 
0.1%
16.72 11
 
0.1%
38.47 11
 
0.1%
Other values (4848) 19878
99.4%
ValueCountFrequency (%)
0.51 3
< 0.1%
0.52 4
< 0.1%
0.53 5
< 0.1%
0.54 5
< 0.1%
0.55 3
< 0.1%
0.56 3
< 0.1%
0.57 3
< 0.1%
0.58 1
 
< 0.1%
0.59 4
< 0.1%
0.61 3
< 0.1%
ValueCountFrequency (%)
50 2
 
< 0.1%
49.99 2
 
< 0.1%
49.98 8
< 0.1%
49.97 5
< 0.1%
49.96 4
< 0.1%
49.95 6
< 0.1%
49.94 8
< 0.1%
49.93 2
 
< 0.1%
49.92 1
 
< 0.1%
49.91 7
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Semanal
6676 
Mensual
6663 
Diaria
6661 

Length

Max length7
Median length7
Mean length6.66695
Min length6

Characters and Unicode

Total characters133339
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSemanal
2nd rowMensual
3rd rowDiaria
4th rowSemanal
5th rowDiaria

Common Values

ValueCountFrequency (%)
Semanal 6676
33.4%
Mensual 6663
33.3%
Diaria 6661
33.3%

Length

2024-09-03T15:54:29.991319image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:30.059204image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
semanal 6676
33.4%
mensual 6663
33.3%
diaria 6661
33.3%

Most occurring characters

ValueCountFrequency (%)
a 33337
25.0%
e 13339
10.0%
n 13339
10.0%
l 13339
10.0%
i 13322
 
10.0%
S 6676
 
5.0%
m 6676
 
5.0%
M 6663
 
5.0%
s 6663
 
5.0%
u 6663
 
5.0%
Other values (2) 13322
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 33337
25.0%
e 13339
10.0%
n 13339
10.0%
l 13339
10.0%
i 13322
 
10.0%
S 6676
 
5.0%
m 6676
 
5.0%
M 6663
 
5.0%
s 6663
 
5.0%
u 6663
 
5.0%
Other values (2) 13322
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 33337
25.0%
e 13339
10.0%
n 13339
10.0%
l 13339
10.0%
i 13322
 
10.0%
S 6676
 
5.0%
m 6676
 
5.0%
M 6663
 
5.0%
s 6663
 
5.0%
u 6663
 
5.0%
Other values (2) 13322
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 33337
25.0%
e 13339
10.0%
n 13339
10.0%
l 13339
10.0%
i 13322
 
10.0%
S 6676
 
5.0%
m 6676
 
5.0%
M 6663
 
5.0%
s 6663
 
5.0%
u 6663
 
5.0%
Other values (2) 13322
 
10.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
No
10046 
Sí
9954 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters40000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowNo
5th rowSí

Common Values

ValueCountFrequency (%)
No 10046
50.2%
Sí 9954
49.8%

Length

2024-09-03T15:54:30.128215image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T15:54:30.190126image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
no 10046
50.2%
sí 9954
49.8%

Most occurring characters

ValueCountFrequency (%)
N 10046
25.1%
o 10046
25.1%
S 9954
24.9%
í 9954
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 10046
25.1%
o 10046
25.1%
S 9954
24.9%
í 9954
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 10046
25.1%
o 10046
25.1%
S 9954
24.9%
í 9954
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 10046
25.1%
o 10046
25.1%
S 9954
24.9%
í 9954
24.9%

Interactions

2024-09-03T15:54:24.819050image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.330752image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.798893image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.198539image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.586959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.980276image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.381739image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.876904image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.394073image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.858279image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.256141image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.644861image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.041122image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.439718image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.934105image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.453235image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.916320image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.313520image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.701974image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.100253image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.548939image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.988053image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.509530image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.971019image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.365875image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.756561image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.156063image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.601545image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:25.044073image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.567513image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.027711image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.421845image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.812451image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.212875image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.655656image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:25.100915image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.628144image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.086948image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.478325image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.869697image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.270312image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.712612image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:25.156056image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:22.741951image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.141807image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.532494image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:23.925462image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.325788image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-09-03T15:54:24.764300image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-09-03T15:54:30.262666image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Adherencia_MedicaApoyo_SocialCalidad_de_ViviendaComorbilidadesCondicion_LaboralCondiciones_CronicasConsumo_de_AlcoholConsumo_de_TabacoDistancia_Al_HospitalDuracion_Hospitalizacion_DiasEdadEstado_MentalEstado_NutricionalFrecuencia_Actividad_FisicaFrecuencia_de_Visitas_FamiliaresGeneroHistorial_de_AlergiasHistorial_de_ReingresosIndice_de_Masa_CorporalMotivo_HospitalizacionNivel_de_EducacionNumero_DiagnosticosNumero_Visitas_Urgencias_Ultimo_AñoNumero_de_Consultas_Post_AltaPrescripcion_MedicamentosReingresoSoporte_FamiliarTiempo_de_Recuperacion_En_DomicilioTipo_Seguimiento_Post_HospitalizacionTipo_de_Seguro
Adherencia_Medica1.0000.0000.0000.0000.0090.0170.0000.0060.0160.0000.0000.0030.0000.0030.0000.0000.0130.0000.0000.0000.0160.0000.0090.0000.0000.0000.0000.0000.0040.000
Apoyo_Social0.0001.0000.0000.0080.0000.0000.0050.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0020.0000.0000.0000.0060.0000.0040.0000.000
Calidad_de_Vivienda0.0000.0001.0000.0000.0000.0120.0070.0060.0080.0040.0000.0000.0000.0000.0080.0040.0000.0000.0130.0060.0060.0070.0040.0000.0110.0070.0000.0000.0000.000
Comorbilidades0.0000.0080.0001.0000.0000.0070.0060.0150.0090.0120.0100.0080.0000.0000.0000.0000.0000.0000.0080.0100.0000.0000.0110.0000.0140.0000.0120.0000.0000.000
Condicion_Laboral0.0090.0000.0000.0001.0000.0090.0000.0000.0120.0000.0090.0000.0000.0000.0000.0000.0000.0000.0080.0040.0000.0000.0000.0000.0000.0000.0050.0000.0040.000
Condiciones_Cronicas0.0170.0000.0120.0070.0091.0000.0070.0000.0130.0000.0120.0030.0000.0000.0060.0090.0000.0060.0000.0000.0080.0040.0000.0000.0000.0220.0000.0150.0000.005
Consumo_de_Alcohol0.0000.0050.0070.0060.0000.0071.0000.0000.0160.0000.0060.0000.0000.0000.0170.0000.0050.0040.0030.0000.0000.0080.0080.0060.0000.0080.0030.0000.0080.000
Consumo_de_Tabaco0.0060.0000.0060.0150.0000.0000.0001.0000.0000.0040.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0200.000
Distancia_Al_Hospital0.0160.0000.0080.0090.0120.0130.0160.0001.000-0.0030.0100.0000.0110.0120.0000.0080.0000.0000.0100.0000.0030.0000.0000.0120.0190.0000.007-0.0030.0000.003
Duracion_Hospitalizacion_Dias0.0000.0000.0040.0120.0000.0000.0000.004-0.0031.000-0.0130.0080.0000.0000.0000.0000.0060.0100.0140.0140.0110.0000.005-0.0090.0100.0070.0040.0060.0000.000
Edad0.0000.0050.0000.0100.0090.0120.0060.0000.010-0.0131.0000.0000.0000.0000.0000.0090.0080.0000.0010.0090.0120.010-0.0060.0090.0000.0000.008-0.0010.0000.016
Estado_Mental0.0030.0000.0000.0080.0000.0030.0000.0000.0000.0080.0001.0000.0000.0030.0110.0000.0000.0000.0000.0000.0000.0100.0140.0000.0000.0000.0050.0000.0020.000
Estado_Nutricional0.0000.0000.0000.0000.0000.0000.0000.0040.0110.0000.0000.0001.0000.0050.0000.0000.0000.0000.0110.0080.0000.0050.0180.0110.0000.0000.0000.0000.0100.000
Frecuencia_Actividad_Fisica0.0030.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0030.0051.0000.0000.0150.0000.0140.0150.0000.0000.0000.0130.0000.0000.0250.0000.0000.0000.005
Frecuencia_de_Visitas_Familiares0.0000.0000.0080.0000.0000.0060.0170.0000.0000.0000.0000.0110.0000.0001.0000.0000.0000.0000.0000.0000.0100.0000.0130.0120.0000.0060.0030.0120.0010.000
Genero0.0000.0000.0040.0000.0000.0090.0000.0000.0080.0000.0090.0000.0000.0150.0001.0000.0000.0000.0000.0090.0140.0140.0000.0160.0000.0000.0000.0000.0060.000
Historial_de_Alergias0.0130.0000.0000.0000.0000.0000.0050.0000.0000.0060.0080.0000.0000.0000.0000.0001.0000.0000.0000.0100.0120.0000.0000.0130.0000.0000.0000.0000.0000.008
Historial_de_Reingresos0.0000.0000.0000.0000.0000.0060.0040.0000.0000.0100.0000.0000.0000.0140.0000.0000.0001.0000.0000.0050.0060.0000.0000.0000.0000.0000.0000.0000.0040.013
Indice_de_Masa_Corporal0.0000.0000.0130.0080.0080.0000.0030.0000.0100.0140.0010.0000.0110.0150.0000.0000.0000.0001.0000.0090.0000.000-0.0040.0040.0160.0000.0000.0030.0000.010
Motivo_Hospitalizacion0.0000.0090.0060.0100.0040.0000.0000.0000.0000.0140.0090.0000.0080.0000.0000.0090.0100.0050.0091.0000.0120.0000.0030.0070.0000.0000.0060.0000.0000.013
Nivel_de_Educacion0.0160.0000.0060.0000.0000.0080.0000.0000.0030.0110.0120.0000.0000.0000.0100.0140.0120.0060.0000.0121.0000.0000.0000.0120.0000.0000.0060.0110.0000.000
Numero_Diagnosticos0.0000.0020.0070.0000.0000.0040.0080.0000.0000.0000.0100.0100.0050.0000.0000.0140.0000.0000.0000.0000.0001.0000.0000.0030.0000.0150.0000.0090.0000.000
Numero_Visitas_Urgencias_Ultimo_Año0.0090.0000.0040.0110.0000.0000.0080.0000.0000.005-0.0060.0140.0180.0130.0130.0000.0000.000-0.0040.0030.0000.0001.000-0.0030.0160.0170.000-0.0030.0000.015
Numero_de_Consultas_Post_Alta0.0000.0000.0000.0000.0000.0000.0060.0000.012-0.0090.0090.0000.0110.0000.0120.0160.0130.0000.0040.0070.0120.003-0.0031.0000.0000.0000.009-0.0100.0000.000
Prescripcion_Medicamentos0.0000.0000.0110.0140.0000.0000.0000.0000.0190.0100.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0160.0001.0000.0060.0000.0000.0000.000
Reingreso0.0000.0060.0070.0000.0000.0220.0080.0110.0000.0070.0000.0000.0000.0250.0060.0000.0000.0000.0000.0000.0000.0150.0170.0000.0061.0000.0000.0180.0000.018
Soporte_Familiar0.0000.0000.0000.0120.0050.0000.0030.0000.0070.0040.0080.0050.0000.0000.0030.0000.0000.0000.0000.0060.0060.0000.0000.0090.0000.0001.0000.0040.0000.000
Tiempo_de_Recuperacion_En_Domicilio0.0000.0040.0000.0000.0000.0150.0000.000-0.0030.006-0.0010.0000.0000.0000.0120.0000.0000.0000.0030.0000.0110.009-0.003-0.0100.0000.0180.0041.0000.0000.000
Tipo_Seguimiento_Post_Hospitalizacion0.0040.0000.0000.0000.0040.0000.0080.0200.0000.0000.0000.0020.0100.0000.0010.0060.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.011
Tipo_de_Seguro0.0000.0000.0000.0000.0000.0050.0000.0000.0030.0000.0160.0000.0000.0050.0000.0000.0080.0130.0100.0130.0000.0000.0150.0000.0000.0180.0000.0000.0111.000

Missing values

2024-09-03T15:54:25.277995image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-03T15:54:25.479461image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EdadGeneroDuracion_Hospitalizacion_DiasNumero_Visitas_Urgencias_Ultimo_AñoNumero_DiagnosticosCondiciones_CronicasTipo_Seguimiento_Post_HospitalizacionAdherencia_MedicaSoporte_FamiliarReingresoTipo_de_SeguroNivel_de_EducacionIndice_de_Masa_CorporalEstado_MentalFrecuencia_Actividad_FisicaConsumo_de_TabacoConsumo_de_AlcoholCondicion_LaboralCalidad_de_ViviendaHistorial_de_ReingresosPrescripcion_MedicamentosTiempo_de_Recuperacion_En_DomicilioApoyo_SocialMotivo_HospitalizacionNumero_de_Consultas_Post_AltaEstado_NutricionalComorbilidadesDistancia_Al_HospitalFrecuencia_de_Visitas_FamiliaresHistorial_de_Alergias
06912991NingunaAltoBajaModerado12Sin educación18.98Sin problemas conocidosAltaFumadorAltoEmpleadoMala3No23BajoEnfermedad crónica72Sin comorbilidades16.80SemanalSí
13201334Tres o másNingunoBajaBueno11Primaria17.28AnsiedadModeradaNo fumadorModeradoEmpleadoMala1Sí59BajoEmergencia60Diabetes6.30MensualSí
28912434Tres o másBajoBuenaBajo10Primaria22.70DepresiónNingunaFumadorAltoDesempleadoBuena2Sí30AltoEnfermedad crónica00Hipertensión10.07DiariaSí
3780321UnaNingunoModeradaModerado00Sin educación31.70AnsiedadBajaNo fumadorNingunoDesempleadoBuena2Sí10AltoEmergencia70Diabetes19.38SemanalNo
43811532UnaAltoBajaBueno12Sin educación16.29DepresiónBajaNo fumadorAltoDesempleadoMala2Sí5AltoEmergencia32Hipertensión6.01DiariaSí
5410382UnaAltoBuenaBajo01Secundaria38.22AnsiedadModeradaFumadorModeradoJubiladoRegular1No45BajoCirugía42Hipertensión45.88SemanalSí
62012341DosBajoModeradaBueno02Primaria25.03DepresiónModeradaNo fumadorNingunoDesempleadoMala0Sí56BajoEmergencia80Sin comorbilidades10.28SemanalNo
73911054Tres o másAltoBajaModerado02Primaria22.14AnsiedadNingunaNo fumadorBajoJubiladoMala0No18AltoCirugía01Hipertensión10.48SemanalNo
8701632DosBajoBuenaBajo11Universitaria35.35Sin problemas conocidosNingunaFumadorBajoJubiladoRegular4No21BajoCirugía22Hipertensión34.44DiariaNo
91902302Tres o másBajoModeradaBajo00Sin educación36.83AnsiedadAltaNo fumadorAltoJubiladoBuena0Sí19AltoEnfermedad crónica51Hipertensión41.41DiariaNo
EdadGeneroDuracion_Hospitalizacion_DiasNumero_Visitas_Urgencias_Ultimo_AñoNumero_DiagnosticosCondiciones_CronicasTipo_Seguimiento_Post_HospitalizacionAdherencia_MedicaSoporte_FamiliarReingresoTipo_de_SeguroNivel_de_EducacionIndice_de_Masa_CorporalEstado_MentalFrecuencia_Actividad_FisicaConsumo_de_TabacoConsumo_de_AlcoholCondicion_LaboralCalidad_de_ViviendaHistorial_de_ReingresosPrescripcion_MedicamentosTiempo_de_Recuperacion_En_DomicilioApoyo_SocialMotivo_HospitalizacionNumero_de_Consultas_Post_AltaEstado_NutricionalComorbilidadesDistancia_Al_HospitalFrecuencia_de_Visitas_FamiliaresHistorial_de_Alergias
199906601472Tres o másAltoBajaBueno00Sin educación34.76DepresiónBajaFumadorAltoEmpleadoBuena1No34MedioEmergencia50Hipertensión14.47DiariaSí
19991701303UnaAltoBuenaBajo00Secundaria18.66AnsiedadAltaFumadorNingunoEmpleadoRegular2No46BajoEmergencia22Diabetes37.36MensualSí
199924801074NingunaNingunoBuenaModerado12Primaria32.84AnsiedadAltaFumadorNingunoDesempleadoBuena0Sí5MedioEmergencia10Cardiopatía4.03MensualNo
19993290983DosNingunoBuenaBueno02Primaria19.97DepresiónModeradaFumadorAltoJubiladoBuena2Sí48MedioCirugía20Cardiopatía49.62DiariaSí
199947502144DosAltoModeradaModerado01Sin educación23.11AnsiedadModeradaFumadorModeradoJubiladoRegular2No13MedioCirugía61Hipertensión23.99DiariaSí
19995531761NingunaMedioBuenaBueno12Universitaria16.22Sin problemas conocidosBajaNo fumadorModeradoEmpleadoRegular4Sí29MedioCirugía42Sin comorbilidades13.54MensualSí
199962702693DosAltoBajaModerado02Sin educación23.08Sin problemas conocidosNingunaFumadorBajoEmpleadoBuena2Sí49MedioEmergencia21Diabetes48.74MensualSí
19997231713DosMedioBuenaBueno12Secundaria23.39DepresiónBajaNo fumadorModeradoDesempleadoBuena1No57MedioCirugía61Sin comorbilidades41.41MensualNo
19998240703NingunaAltoModeradaModerado01Sin educación38.58DepresiónModeradaFumadorNingunoDesempleadoBuena2Sí28AltoEmergencia92Hipertensión23.54MensualSí
199994811253DosNingunoModeradaBueno01Universitaria21.53DepresiónBajaFumadorModeradoJubiladoBuena4No46AltoCirugía21Cardiopatía27.72SemanalSí